The model is:
probit Y ib2.D i.supplier##i.S i.B##i.supplier i.B##i.S i.S#c.N i.supplier#c.N ib1.income ib1.employment i.children i.gender ib1.education ib2.age
Where N takes the value 1,2,3 or 4, and B takes the value 0 or 1.
margins, dydx(B) at(S=(1 2 3 4) supplier=(1 2 3 4 5 6) N=1 D=3 income=4 employment=1 children=0 gender=0 age=3 education=1)
gives dS/dB, and...
margins, dydx(N) at(S=(1 2 3 4) supplier=(1 2 3 4 5 6) B=1 D=3 income=4 employment=1 children=0 gender=0 age=3 education=1)
gives dS/dN
and for all possible S*supplier combinations:
dS/dB + dS/dN = [margins, at(S=(1 2 3 4) supplier=(1 2 3 4 5 6) N=2 B=1 D=3 income=4 employment=1 children=0 gender=0 age=3 education=1)] - [margins, at(S=(1 2 3 4) supplier=(1 2 3 4 5 6) N=1 B=0 D=3 income=4 employment=1 children=0 gender=0 age=3 education=1)]
But, the problem I have is in finding the standard error associated with the combined (dS/dB + dS/dN) term and I was wondering whether Stata has some way of calculating the standard error associated with the compound effect of a marginal change in several variables.
I hope this makes sense now!
Many thanks for your help!
Tim
--
Tim Burnett
PhD Researcher
Department of Economics & ESRC Centre for Competition Policy
University of East Anglia
Email: tim.burnett@uea.ac.uk
Phone: +44 (0) 7793 116 522
Skype: timb318
________________________________________
From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of Nick Cox [njcoxstata@gmail.com]
Sent: 12 November 2012 18:20
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Several questions regarding xtprobit and margins command
I'll comment on your problem #1.
The help for -margins- starts
"margins [marginlist] [if] [in] [weight] [, response_options options]
where marginlist is a list of factor variables or interactions that appear
in the current estimation results."
When you give arguments immediately after the command, the crucial part is
"margins marginlist ...
where marginlist is a list of factor variables or interactions that appear
in the current estimation results."
So, it would help if you gave the exact and complete -xtprobit-
command you used. I suspect that the error message will make sense
when we see the exact model you fitted.
Nick
P.S. "dice" is a strange word even to those for whom English is a
first language. "dice" is a plural: the singular is "die". One die,
two dice.
On Mon, Nov 12, 2012 at 2:58 PM, Tobias Morville
<tobiasmorville@gmail.com> wrote:
> I have a set of questions regarding the margins command, and marginal
> effects in general.
>
> I have a unbalanced paneldataset of 4124 observations, unevenly
> distributed on 18 subjects.
>
> My model is as follows: P(stop) = Outcome outcome_lag1 seqEarn, which
> im estimating in a RE probit setting with xtprobit command.
>
> Outcome: Outcome of a dice in period t. Lies from 1 to 6
> Outcome_lag1: Outcome of the dice in period t-1
> seqEarn: Accumulated earnings over each game. Drops to 0 if subject
> chooses to stop, or the dice shows a one. Starts at zero, and can only
> get more positive as people climb the reward ladder.
>
> All of these regressors are significant.
>
> Sooo, now the questions begin:
>
> 1) If i use -margins Outcome- (followed this guide
> http://www.stata.com/stata12/margins-plots/), then i get this
> errormessage: "'Outcome' not found in list of covariates", and that
> actually is the case for all margins commands, and is my number one
> headache.
>
> The only marginscommand that works, is if i use the -margins,
> dydx(Outcome outcome_lag1 seqEarn)-, which leads me to my next
> problem:
>
> 2) When i use a -margins, dydx(Outcome outcome_lag1 seqEarn)- my
> marginal effects are exactly the same as my regression coefficients?
>
> If i change the code to -margins, dydx(Outcome outcome_lag1 seqEarn)
> atmeans- they're the same again..?
> (So APE = MEM?)
>
> Im really confused about this, and i've read the ealier post
> (http://www.stata.com/statalist/archive/2009-11/msg01517.html), which
> covers some of the questions i have, but dosen't really anwser them.
>
> If i use mfx compute, predict(pu0) they change, but they become very
> small. And im guess that pu0 means that Im setting the random effects
> slope to 9, which is a bad idea for my data, as there is quite alot of
> random variability.
>
> 3) If i choose to ignore the fact that my marginal effects are the
> same as my probit regression coefficients, then im in my next pickle.
> That my marginal effects are constant. If i plot the predicted
> probability of stopping the game, over seqEarn, its constant, which
> suits my data very badly. And im afraid that i've misunderstood
> something very basic.
>
> if i try -margins, dydx(seqEarn) at (Outcome=(2 3 4 5 6)) vsquish-
> marginal effects are the same over Outcome size...
>
> .. and it's basically the same.
>
> What i would idealy like to see, is that predicted probability changes
> both over seqEarn and over Outcome and outcome_lag1 in some systematic
> way, but right now my newbieness in Stata problemshooting is driving
> me up the wall.
>
> ------
>
> Background (just for the interested):
>
> I'm currently working with a dataset of 18 subjects, playing a virtual
> dicegame for 25 mins while in a fMRI scanner. The dice is random
> (1-6), and if you roll one, you lose whatever you accumulated this
> round. It's a balloon kind of thing: How far do people dare to go up
> the exponential reward ladder, before banking their earnings.
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